Borrowing it
Nothing to install: this file belongs to Eduardo-Salvador/Agent-Harness-Kit. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Eduardo-Salvador/Agent-Harness-Kit/main/.agents/skills/feature-discovery/SKILL.mdgit clone --depth 1 https://github.com/Eduardo-Salvador/Agent-Harness-KitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/eduardo-salvador/agent-harness-kit/feature-discovery)<a href="https://agentmods.dev/skills/eduardo-salvador/agent-harness-kit/feature-discovery"><img src="https://agentmods.dev/badge/skills/eduardo-salvador/agent-harness-kit/feature-discovery/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/eduardo-salvador/agent-harness-kit/feature-discovery"><img src="https://agentmods.dev/badge/skills/eduardo-salvador/agent-harness-kit/feature-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00066 | $0.00676 |
| Opus 5 | $0.00033 | $0.00338 |
| Sonnet 5 | $0.00013 | $0.00135 |
| Haiku 4.5 | $0.00007 | $0.00068 |
Grade A, and why
feature-discovery scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Feature discovery
- Read
../../../harness/playbooks/feature-discovery.mdand follow it before creating or changing technical graph nodes. - Require approved project context. If it is missing, stale, or unapproved, route to
first-run-discoveryinstead; do not run two interviews at once. - Activate without requiring the user to name this skill when the request introduces a new product capability and leaves consequential choices open. Also activate when graph planning or expansion exposes an unresolved functionality boundary or completion condition; an initial system brief is not blanket scope approval. Explicit brainstorming, ideation, "what if", "how could we", and feature exploration always qualify.
- Do not activate for a bug fix, maintenance/refactor, dependency update, direct implementation of an approved brief, or a
direct-trivialcopy/style/static-content edit. The fast path precedes project discovery and creates no feature artifact. - Start by reflecting known project evidence and ask exactly one highest-leverage unanswered feature question. Never repeat the Harness welcome or ask the user to restate approved context.
- Analyze the feature for consequential gaps before proposing implementation: intended and excluded actors, eligibility and permissions, entry/onboarding, the happy path, alternate paths, failure states, cancellation, recovery, empty states, data lifecycle, integrations, privacy/security, and observable success. Ask only the branches that apply. For authentication, for example, resolve email versus federated login, verification, forgotten-password recovery, account conflicts, provider failure, session/logout behavior, and who may access what.
- Present two to four credible directions with tradeoffs and one recommendation. Do not silently select scope, UX, data, integration, architecture, or risk decisions for the user. State concrete "This task is complete only when..." conditions for the next bounded functionality; help the client resolve unclear rules with examples instead of deciding silently. A feature is not closed while a consequential journey, failure, recovery path, or acceptance outcome remains unresolved.
- Persist the result in
../../../harness-state/features/FEATURE-<id>.md. MaterializeFEATURE-BRIEFthrough the runtime template catalog only for that consumer, and keep itdraftuntil the user explicitly approves the selected direction. - Do not add unapproved feature scope to
PENDING.mdorTASK-GRAPH.mdbefore feature approval. If existing execution exposes an open condition, record the exact human blocker and keep the existing node pending/blocked withscope_status: needs-discovery; this records a stop, not new scope. After approval, update macro scope in pending state when applicable; when planning or implementation was requested, route throughwriting-plansbefore graph creation. - In hackathon mode, ask at most two cohesive feature questions before offering directions and converge on one demonstrable vertical slice; record deferred edge cases and shortcuts explicitly instead of pretending they were solved.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago Changed af4cb544f084
- 12d ago First seen · 18 lines · 66 tokens per session scan A 11360b18046c
feature-discovery is a skill published in the GitHub repository Eduardo-Salvador/Agent-Harness-Kit (6 stars, last pushed 3d ago), licensed MIT. It adds 66 tokens to every session and 676 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
qa
Run scalable, isolated live QA for nac development. The top-level local orchestrator must parse n (default 4), dispatch one setup worker with this skill, copy its n assignment contracts verbatim into exactly n parallel test workers with this skill, then dispatch one aggregate worker with this skill using all test…
release
Cut and publish a full stable NAC release after main, release-PR, and publication CI pass. Use when a maintainer asks for a stable version bump, tag, or GitHub Release. Never use for release candidates; NAC RC releases are automated.
openrig-user
Use when a specific rig command, subcommand, or flag is already known and you need its exact syntax, JSON shape, defaults, or error meaning. NOT for natural capability discovery, open-ended how-do-I questions, or choosing which OpenRig move applies.
triage
Triage a GitHub repository's open issues by finding exact duplicates, rejecting evidenceably off-base requests, requesting concrete clarification, applying only existing labels, and opening a linked root-cause issue when multiple reports share one underlying invariant failure. Use when a maintainer asks to triage…
orienting-to-an-inherited-seat
Use when you have just been primed into an EXISTING seat through a planned handover — a different agent retired and handed you the seat plus its earned context — and you need a world model of what just happened to you. Covers how a handover differs from compaction and from a fresh launch, the…
forming-an-openrig-mental-model
Use when the system around you does not make sense yet: you just booted into a seat and do not know how the pieces fit; someone said rig, pod, seat, fleet, topology, or slice and you are not certain what they mean here; you are unsure what kind of rig you are in or what it is for; you do not know how skills reach you…